{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "18bfedd4-5296-4246-b380-43a5e8b4c284",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import json\n",
    "import numpy as np\n",
    "import seaborn\n",
    "\n",
    "seaborn.set_context(\"talk\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "581e5007-485a-4eb5-9ce2-1ee90567da0f",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "def parse_log(fname):\n",
    "    with open(fname) as f:\n",
    "        lines = f.readlines()\n",
    "\n",
    "    messages = []\n",
    "    for line in lines:\n",
    "        try:\n",
    "            messages.append(json.loads(line.strip()))\n",
    "        except json.JSONDecodeError:\n",
    "            pass\n",
    "    return messages\n",
    "\n",
    "\n",
    "def plot_wait_time(messages, ax, title=\"Time waiting\"):\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "\n",
    "    wait_times = []\n",
    "    end = None\n",
    "    for m in messages:\n",
    "        if m[\"event\"] == \"training end\":\n",
    "            end = m[\"time\"]\n",
    "        if m[\"event\"] == \"training start\" and end is not None:\n",
    "            wait_times.append(m[\"time\"] - end)\n",
    "\n",
    "    wait_times = np.array(wait_times)\n",
    "    max_show = wait_times.mean() + 3 * wait_times.std()\n",
    "\n",
    "    print(\"average wait time\", wait_times.mean())\n",
    "\n",
    "    ax.hist(wait_times, bins=np.linspace(0, max_show, 100), color=\"#6D0EDB\")[-1]\n",
    "    ax.set_xlabel(\"time (sec)\")\n",
    "\n",
    "\n",
    "def plot_log(messages, ax, title=\"\"):\n",
    "    origin = messages[0][\"time\"]\n",
    "    assert messages[0][\"event\"] == \"run start\"\n",
    "\n",
    "    rows = {\"setup\": 3, \"get-batch\": 2, \"train\": 1, \"epoch\": 0}\n",
    "\n",
    "    ax.set_yticks(list(rows.values()), labels=list(rows))\n",
    "\n",
    "    if title:\n",
    "        ax.set_title(title)\n",
    "\n",
    "    data = {\"batches\": []}\n",
    "\n",
    "    for m in messages:\n",
    "        t = m[\"time\"] - origin\n",
    "\n",
    "        if m[\"event\"] == \"setup end\":\n",
    "            ax.barh(\n",
    "                rows[\"setup\"],\n",
    "                m[\"duration\"],\n",
    "                left=t - m[\"duration\"],\n",
    "                edgecolor=\"k\",\n",
    "                linewidth=0.1,\n",
    "                color=\"#6D0EDB\",\n",
    "                zorder=1,\n",
    "            )\n",
    "\n",
    "        if m[\"event\"] == \"get-batch end\":\n",
    "            ax.barh(\n",
    "                rows[\"get-batch\"],\n",
    "                m[\"duration\"],\n",
    "                left=t - m[\"duration\"],\n",
    "                # alpha=0.5,\n",
    "                color=\"#C396F9\",\n",
    "                zorder=1,\n",
    "            )\n",
    "            data[\"batches\"].append(m[\"duration\"])\n",
    "\n",
    "        if m[\"event\"] == \"training end\":\n",
    "            ax.barh(\n",
    "                rows[\"train\"],\n",
    "                m[\"duration\"],\n",
    "                left=t - m[\"duration\"],\n",
    "                color=\"#FF6554\",\n",
    "                zorder=1,  # edgecolor=\"k\", linewidth=0.1,\n",
    "            )\n",
    "\n",
    "        if m[\"event\"] == \"epoch end\":\n",
    "            ax.barh(\n",
    "                rows[\"epoch\"],\n",
    "                m[\"duration\"],\n",
    "                left=t - m[\"duration\"],\n",
    "                edgecolor=\"k\",\n",
    "                linewidth=0.1,\n",
    "                color=\"#FF9E0D\",\n",
    "                zorder=1,\n",
    "            )\n",
    "\n",
    "    ax.grid(axis=\"x\", zorder=0, alpha=0.5)\n",
    "    ax.set_xlabel(\"time (sec)\")\n",
    "\n",
    "    print(\"average batch duration\", np.mean(data[\"batches\"]))\n",
    "\n",
    "\n",
    "def plot(fname):\n",
    "    messages = parse_log(fname)\n",
    "    fig, axes = plt.subplots(ncols=2, nrows=1, figsize=(16, 6), width_ratios=[3, 1], dpi=400)\n",
    "\n",
    "    plot_log(messages, axes[0], title=\"\")\n",
    "    plot_wait_time(messages, axes[1], title=\"\")\n",
    "\n",
    "    for m in messages:\n",
    "        if m[\"event\"] == \"run start\":\n",
    "            text_str = \"\\n\".join([f\"{k}: {v}\" for k, v in m[\"locals\"].items() if v is not None])\n",
    "            props = dict(boxstyle=\"round\", facecolor=\"#F5F5F5\", alpha=0.5)\n",
    "            fig.text(\n",
    "                0.5,\n",
    "                -0.03,\n",
    "                text_str,\n",
    "                fontsize=14,\n",
    "                horizontalalignment=\"center\",\n",
    "                verticalalignment=\"top\",\n",
    "                bbox=props,\n",
    "            )\n",
    "            break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "781a5d57-3992-47d5-84a6-e7396fc4f948",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average batch duration 0.3457612317057946\n",
      "average wait time 1.3834948765587378\n"
     ]
    },
    {
     "data": {
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O+RpxQ7ke/fns0fZoAACApuidd96JoUOHxpo1a3Kq79KlS5xzzjnx6KOPxsKFC2P16tVRXV0dixcvjvHjx8fVV18dPXr0yOmsDRs2xEknnRTTp09vyEsAAIAmqdj3CAEauy222CL233//OPnkk+PHP/5xPPLIIzFr1qxYtGhRrF69OtavXx+rVq2K2traWLp0afz1r3+NP/zhD3HppZdG3759o6ysLOdedXV1ccopp8TEiRML+IqKq6yurq6u1EMAAAAAQETEm2++GVVVVaUe4xP33ntvDB8+vGDn/+IXv4jvfe97OdV+85vfjOuuuy6n78+8efPiwgsvjAcffDCxtqysLP74xz/G4MGDc5pjc7z99tux1157xQcffJBY271797jhhhtiyJAhibXV1dVxyy23xJVXXhmrV69OrB8+fHjce++9uYwMANAgHTt2jOXLlxe0x/z586Nbt24F7ZELO+2/auw77fDhw+P+++9PrKuoqIgLL7wwLr744mjXrl1i/YQJE+Kcc86JadOmJdZus802MWXKlPjiF7+Y08wAQNPQHPbovn37xjPPPFOy/v9o0KBBMW7cuIL2sEf/K3s0AJAPe++9d0yePDnn+h133DH23Xff6NOnT+y2227xpS99Kbp06RJt2rSJTCYTS5cujaVLl8a8efNi4sSJMWHChHj55ZfrNdM222wTL7zwQuy00071fTk5cz36X9mjP589GgAAoPGrra2Nvn37xqRJkxJr27ZtG1dccUX813/9V7Rs2TKx/g9/+ENccMEFMX/+/MTanXfeOSZPnpzTz6MATVVzeJ4DoDForvcIAdJu1apV/3LdoE2bNnHAAQfEQQcdFAcddFB89atfzemaxaYsWLAgRo0aFTfccEPOH37btWvXeO2116J9+/ab3TctBOYCAAAAkBq5BOY2lRvgb7/9duy2226Jb0LKZDLx05/+NE4//fR697jtttvirLPOig0bNmSt+8IXvhBvvPFGtGnTpt49cnHkkUfGI488klh32GGHxT333BMdOnSo1/mvv/56DBkyJObOnZtY++CDD+b0RjEAgIbY3AdD27ZtG1/5yldi6tSpiXtiGvZiO+2/auw77YMPPhhDhw5NrOvSpUv84Q9/iP33379e51dXV8f3vve9GDVqVGLtkUceGQ899FC9zgcAGrfmsEcnBeZefvnlccUVVxRvoAKzR3+WPRoAyJekN8NmMpno169fDBs2LA455JDN2oGnTZsW119/fdx7772J12f/bq+99oqXXnopKioq6t0vievR/8oenZ09GgAAoHG76aab4rzzzkus23nnnePhhx+OHj161Ov8jz76KI477rgYO3ZsYu3ZZ58dP/nJT+p1PkBT0hye5wBoDJrjPUKAxuDvgbnbbLNNHHHEEfEf//EfMXDgwNhiiy3y3uutt96KM844Ix577LGc6i+++OL40Y9+lPc5ik1gLgAAAACp0ZwCc48++uh44IEHstaUl5fH3XffHccee+xm97n77rvjpJNOiqTLgBdddFGMHDlys/tsyuOPPx6HHXZYYt0RRxwRY8aM2eybYm+//XYcdNBBiZ/y3q1bt5g5c2a0atVqs/oAAOQilwdD27dvH7169Yo+ffpEnz59onfv3rHrrrtGeXl5dOvWLRYsWJD169OwF9tpP6ux77Rr166NHj16JP5vb5tttokJEybE7rvvvll96urq4rvf/W7cfvvtibVjx46NQw89dLP6AACNT3PYo5tTYK49+rPs0QBAPm3qzbA9evSI008/PY477rjo1KlTXno999xzcfzxx8fChQtzqr/yyivjsssuy0vvf+R69GfZo3NjjwYAAGicFi9eHLvuumusWLEia92OO+4Yzz77bGy//fab1aempiaGDh0af/zjH7PWZTKZmDJlSuyxxx6b1QegsWsOz3MANAbN8R4hQGNQW1sbzz33XBx44IHRokWLgverq6uL73znO3HHHXck1rZq1Srefffd6NixY8HnKiSBuQAAAACkRnMJzH3xxRfja1/7WmLdZZddFldeeWWD+/3whz9M/PSvysrKmDt3bnTt2rXB/f6urq4udt9995g1a1bWuh49esQrr7wSbdq0aVC/6dOnx7777hvr1q3LWnfttdfG97///Qb1AgDI5p8fDO3YsWP07t07evfu/clDoDvvvHOUlZV97tc3hgdD7bSf1RR22pEjR8YPfvCDrDXl5eXxpz/9KQYMGNCgXhs2bIh+/frFxIkTs9b17NkzXnvttU3+fwUAaFqawx7dXAJz7dGfZY8GAPLtn98M+41vfCMuvPDC6Nu3b0H6ffTRR3HYYYfF888/n1jbtm3bmDdvXnTu3Dlv/V2P/ix7dP3YowEAABqf7373u/F///d/WWtatWoVL7/8coNDbFetWhV9+vSJOXPmZK0bPHhwPP744w3qBdBYNYfnOQAag+Z2jxCATaurq4ujjjoqxowZk1h7yy23xJlnnlmEqQqnvNQDAAAAAEBzk/TGqoiPb17l6xMVr7rqqujVq1fWmpqamrjxxhvz0u/vHnroocQ3cpWXl8c999zT4DdyRUTsueeecc011yTW3XTTTVFdXd3gfgAAmzJgwIC46KKL4ne/+13Mmzcvli1bFk899VRcd911MXz48Nhll10a/Ruu7bSfago77bp16+InP/lJYt3ZZ5/d4HCCiIgWLVrEPffcE61bt85aN3PmzHjkkUca3A8AaByawx7dXNijP8seDQAUQllZWRxzzDExffr0+OMf/1iwN8JGfBx+8Nhjj+UUQLNq1ar42c9+ltf+rkd/yh5df/ZoAACAxmXx4sUxevToxLqRI0c2OCw34uNgr3vuuSfxPuTYsWNj2rRpDe4H0Bh5ngMgPZrTPUIANq2srCxuvfXWaNeuXWLtww8/XISJCktgLgAAAAAU0ezZs+PRRx9NrLv55pujRYsWeemZyWTi5ptvTqz75S9/GcuWLctLz4iIH//4x4k1p556auy1115563nWWWfFrrvumrVm8eLFceedd+atJwDAP/v9738fI0eOjKOOOiq6d+9e6nHyzk77WU1hp73zzjtjyZIlWWs6deoUl19+ed56du3aNS666KLEumuvvTZvPQGAdGvqe3RzYo/+lD0aACiEQYMGxauvvhr33XdffPnLXy5Kzy233DIefPDBqKysTKwdNWpU1NXV5aWv69GfZY/ePPZoAACAxuPmm29O/OCWnj17xhlnnJG3nnvvvXeMGDEisc7PjUBz5XkOgHRoTvcIAUi23Xbbxdlnn51Y99xzz8X69euLMFHhCMwFAAAAgCK64447Em/6HHLIIbH//vvnte9BBx0U/fr1y1qzevXquPfee/PS7/XXX48XX3wxa00mk4n//u//zku/v6uoqIgf/vCHiXV33HFHXvsCADQndtpPNZWd9pe//GVizbnnnhsdOnTIW89cz3zhhRdi5syZee0LAEDh2KM/yx4NABTC//zP/0SvXr2K3nennXaKc889N7HurbfeimnTpuWlp+vRn7JHN4w9GgAAIP02bNgQo0aNSqy75JJLIpPJ5LX3pZdemvhhPGPGjMnrh+cAAEB9NKd7hADkZujQoYk11dXVMW/evCJMUzgCcwEAAACgSDZu3JjTm6XOO++8gvTP5dy77747L71+85vfJNYMGzYsdthhh7z0+0fHHntsbLfddllrXnnllZgzZ07eewMANHV22s9qCjvtG2+8EZMnT85a07p16zj99NMb3OuftWvXLk499dTEunz9nQIAUHj26E/ZowGApujCCy9MDI+JiHj66acb3Mv16M+yRzeMPRoAACD9nnjiiViyZEnWmi9+8Ytx1FFH5b13VVVVDBkyJGtNTU1NPPDAA3nvDQAAaVfMe4QA5G6vvfZKvN8fEfHmm28WfpgCEpgLAAAAAEUyYcKEePfdd7PWbL/99jFw4MCC9B88eHB06dIla80LL7wQ8+fPb3CvXN60NmLEiAb3+TwVFRVx3HHHJdb99re/LUh/AICmzE77WU1hp73nnnsSa/793/89ttpqqwb3+jy5fA/t7gAAjYc9+lP2aACgKdp6663jgAMOSKx77bXXGtzL9ejPskc3nD0aAAAg3XL5meyEE06ITCZTkP5+bgQAgM9XzHuEANRPt27dEmtWrFhR+EEKSGAuAAAAABTJ448/nlhz9NFHR3l5YS7btWjRIqdPUx87dmyD+rz22muxcOHCrDXbbLNN9O/fv0F9sjn22GMTa3L5+wAA4LPstJ9qKjttLmcMHz68wX02ZY899og99tgja82CBQti5syZBZsBAID8sEd/lj0aAGiqBgwYkFgzb968BvdxPfpT9uj8sEcDAACkV11dXYwbNy6xrpA/Nw4aNCjxQ1wmTZrU6ENmAABgcxTrHiEA9ZP0QcARERs2bCjCJIUjMBcAAAAAiuSJJ55IrPnGN75R0BlyOf/Pf/5zg3rk8joPOeSQgn2ye0REnz59Ytttt81aM3ny5Fi6dGnBZgAAaIrstJ9qCjvthx9+GFOmTMlaU1lZmdPDbQ1RjL9TAAAKzx79KXs0ANCUfeELX0isWbZsWYP7uB79KXt0/tijAQAA0mnq1Knx/vvvZ63Zfvvt4ytf+UrBZqioqEj8ubS2tjbGjx9fsBkAACCtinWPEID6admyZWJN0gcEpZ3AXAAAAAAogvfeey9mzJiRtaZly5ZxwAEHFHSOgw8+OCorK7PWPP300w36pLAnn3wysabQb3CKiOjfv3/WP9+4caOH1QAA6sFO+1lNYacdP358bNy4MWvNfvvtF23atNnsHrkYOHBgYk0ufycAAJSWPfpT9mgAoClLClaNiFi9enWDerge/Vn26PyxRwMAAKRTWn4+9nMjAAB8vmLcIwSg/pYsWZJYs/XWWxdhksIRmAsAAAAARfDyyy9HXV1d1po+ffpEq1atCjpH69ato3fv3llrVqxYEbNnz97sHi+99FJiTaHftBYRceCBBybW5DIrAAAfs9N+VlPYadPyOvfff/9o0aJF1pqXX3654HMAANAwadkv7dGfskcDAIWQFHoa8XGYbUO4Hv1Z9uj8sUcDAACkU1p+bvQeBAAA+HzFuEcIQP298847Wf88k8lEjx49ijRNYQjMBQAAAIAiePXVVxNr+vTpU4RJcuszZcqUzTp74cKF8eGHH2atad/+/7F331FSl2f/gO8FFpZepUgvKiCgVBU1YgMlYEONsZdETTSWaEw0QYktUYl5NaZoolE0Go0asWFBRQSxoFQBjVQRURSEpbPs/v7wZwwJ7MzOzswOu9d1juc9J3N/n/v+urO+z8x85tkG0aVLl5TWL4tM3icAQFVkT/uNyrKnzZWfaZ06daJr166l1qxYsSKWLl2a8VkAAEiNfXTZZygv+2gAoKJ89tlnCWsaNGhQrh65sudKto/3oxPLlZ+pfTQAAEBuypXXjbvvvnvUr1+/1JpZs2ZFUVFRxmcBAIBcko3PCAEom8WLF8e//vWvUmv69u0bdevWzdJEmeHAXAAAAADIgmQCXL17987CJBF9+vRJWJPMvKlet/fee0deXl5K65dFr169okaNGqXWODAXACB59rTfqCx72unTpyesqQw/UwAAMs8+elv20QBAZTZjxoyENR07dixXD+9Hf8M+Ov3sowEAAHLLl19+GQsXLiy1pkaNGtGjR4+Mz5KXlxd77713qTWbNm2KOXPmZHwWAADIJdn4jBCAshk7dmzCmqFDh2ZhksxyYC4AAAAAZEEygajdd989C5NE7LbbbglrUg1w5dJ91qxZM9q1a1dqzcqVK2P58uVZmQcAYGeXS3s9e9pvpLqnXbZsWaxatarUmlq1akX79u3LvHYqMvkzBQAg8+yjv2EfDQBUdhMmTEhY061bt3L1yKX9pfejv2EfDQAAQCYk8xqsQ4cOUbNmzSxM43UjAABsTzY+IwQgeWvXro2bb7651JpatWrF+eefn6WJMqf0P/0LAAAAADlo7dq18eqrr8aUKVNi5syZMX/+/Pjkk09i3bp1UVxcHHXq1InatWtHy5Yto02bNtGpU6fYe++9o2/fvtGrV6/Iy8vL6rzFxcWxePHihHXJBKvSIZk+if5Ce3muy9Z9ft1rwYIFpdYsXLgwWrZsmaWJAAB2Tva0Ze+fLpna0yZzn506dYpq1bLzd3gz+TMFANiZlJSUxLvvvhuTJ0+Ot99+O/71r3/F4sWLY82aNbFx48YoKCiI2rVrR+PGjaNNmzbRrl276NmzZ/Tu3TsGDBgQ9evXr5C57aO/YR8NAFRmH3zwQcyYMSNh3YEHHphyD+9Hl71/uthHf8M+GgAAIHty8fVxIl43AgBQlWTjM0IAymbkyJHx8ccfl1pz9tlnR/PmzbM0UeY4MBcAAACAncppp50Wb7/9dmzatGmHNWvWrIk1a9bEp59++j8fwuyyyy5x5JFHxne+85044ogjsvJlm2XLlsXmzZtLraldu3bW3nBs2bJlFBQUxMaNG3dYs3jx4igpKSnz4cLJBL86dOhQpjXLo2PHjglrFi5cGPvtt18WpgEA2HnZ026rMuxpd9b7BACozP7xj3/En//851i2bNkOa9avXx/r16+PL774Ij788MNtHsvPz48DDzwwjj766Dj11FOjSZMmmR7533bW/aV9NABA2dxxxx0Ja+rXr1+uz+C9H72tXNxf2kcDAACQTl43AgBAbsvGZ4QAJO9Xv/pV/N///V+pNe3bt49f//rX2Rkow7Lzp3cBAAAAIE0mTZpU6mG5iaxYsSLGjBkT3/72t6NTp05x6623lvqlpnRYtGhRwpqWLVtmdIb/1qJFi1If37RpU6mHMuxIrt1rovuMEFYDAEhGru3zIuxp/1Mqe9qqcp8AADuTOXPmpLSH/dqWLVvi5Zdfjosvvjhat24dZ555ZsyfPz+NE+5YVdlfVpX7BADYnuXLl8c999yTsO6EE06IWrVqpdwn1/ZcEd6P/k/20QAAAKSb140AAJC7svUZIQCJrV69Os4///y46qqrSq2rVatW3H///dGgQYMsTZZZDswFAAAAoMpavHhxXHbZZbHbbrvFQw89lLE+n332WcKabH+ZK5l+K1asKPO6uXavmbpPAICqJtf2ecn2s6fdsapynwAAVdXGjRvjvvvui65du8aFF14YhYWFGe1XVfaXVeU+AQC252c/+1msW7cuYd25555brj65tudKtp/95Y5VlfsEAAAgNV43AgBA7srWZ4QA7NiKFSvi//7v/6Jr165x5513llpbq1ateOyxx+LAAw/M0nSZ58BcAAAAAKq8pUuXxsknnxzHHXdcfPHFF2lfP5k1mzVrlva+pdlll10S1pT138WWLVuSOnQhm/eaifsEAKiK7Gm3VRn2tLn2My0oKIh69eqVWrN69eooKirK0kQAAJVDUVFR/P73v48ePXrEhAkTMtLDPnpb9tEAQGU0YcKEGDNmTMK6IUOGxD777FOuXrm254rwfvR/so8GAAAg3XLtdaPvIAAAwFey+RkhABFr166NZcuWxdy5c2Ps2LExatSoOPLII2PXXXeNSy+9NJYvX17q9S1btoxnnnkmvv3tb2dp4uxwYC4AAAAA/H///Oc/Y5999om5c+emdd2VK1cmrGnQoEFaeyZSv379hDXJzJ1KfTbvNRP3CQBQFdnTbqsy7Gl31p/pqlWrsjAJAEDls2TJkjj88MPjD3/4Q9rXto/eln00AFDZrF69Os4444woKSkpta5atWpx4403lrvfzrrn8n50+a7JxZ+pfTQAAEB25NrrRt9BAACA7H9GCFDZ/eIXv4i8vLxS/6lfv360bt06unfvHsccc0z88pe/jOeeey6pP/Y6YsSImDVrVhx66KFZuJvscmAuAAAAAPyH+fPnx/777x/Tp09P25rJfIEmmVBVOmXiiz/J1BcUFESNGjXKtG55+IITAEB62NN+o7LsaavKzxQAgG8UFRXFBRdcEL/85S/Tuq59dNl7p5N9NACQaeecc04sWbIkYd0Pf/jD6NOnT7n7VZU9l3102Xunk300AABA7si11401a9aMmjVrllqzfv362Lx5c5YmAgCA7Mv2Z4QAlF1eXl4cc8wx8fbbb8ejjz4azZo1q+iRMsKBuQAAAADwX1atWhWHH354zJs3Ly3rbdq0KWFN3bp109IrWcn027hxY5nWrCr3CQBQFVWVvV5Vuc+IqnWvAABsa9SoUfGb3/wmbetVpb1lVbpXAICIiJtuuikee+yxhHXt27ePG264IS09q8qeq6rcZ0TVulcAAADKzutGAADILRXxGSEAyevbt2/ccccdsXTp0vjnP/8Z/fr1q+iRMip7f2YYAAAAAMqhbt26MWDAgOjevXt07949unXrFi1btoz69etHgwYNok6dOrFmzZpYtWpVrFq1KhYtWhRTpkyJ119/Pd59990y//Xuzz//PI499th4++23o169euWaPZneNWpk9626ZPpt2bKlTGtWlfsEAKiKqsper6rcZ0TVulcAgFyXl5cXPXv2jF69ekW3bt2ie/fu0bFjx2jQoMG//9m8eXOsXLkyVq1aFZ999llMnTo1pkyZElOmTIkVK1aUuecVV1wRPXv2jMGDB5d7/qq0t6xK9woAMG7cuPj5z3+esK569erxwAMPRIMGDdLSt6rsuarKfUZUrXsFAACg7LxuBACA3FFRnxECkLzp06fHQw89FF9++WWccMIJsfvuu1f0SBnlwFwAAAAAclbv3r3jyCOPjMMPPzwGDhwYNWvWLLW+SZMm0aRJk4iI6NevXxx//PEREfHll1/G3XffHb///e9j4cKFSfefN29e/PCHP4wxY8akfhORXBAq2wGu/Pz8hDVlPWS4qtwnAEBVVFX2elXlPiOq1r0CAOSiZs2axbBhw+Lwww+Pww47LJo3b15qfX5+ftStWzfatm0bERGHHXZYRESUlJTEuHHj4ne/+108//zzUVJSklT/4uLiOPnkk2POnDkJeydSlfaWVeleAYCqbfr06XHiiSfG1q1bE9aOHDkyDjjggLT1rip7rqpynxFV614BAAAoO68bAQAgN1TkZ4QAJG/r1q0xefLkmDx5clx99dVx1FFHxRVXXBH77bdfRY+WEdUqegAAAAAA+E+77LJLXHrppTFz5sx4991344YbbohBgwYlPCy3NI0aNYrLLrssPvzww/jrX/9apr9YeP/998eECRNS7h0RUVRUlLCmMvzF86pynwAAVVFV2etVlfuMqFr3CgCQK/Lz82PYsGHx2GOPxbJly+Kvf/1rnHzyyeU6sDYvLy+GDh0a48aNi9mzZ8eAAQOSvvaLL76IK664IuXeX6tKe8uqdK8AQNX10UcfxbBhw2Lt2rUJa4cNGxZXX311WvtXlT1XVbnPiKp1rwAAAJSd140AAFDxKvozQgBSU1xcHE888UQMHDgwTj/99Fi9enVFj5R2DswFAAAAIGe0adMmPv7447j11lujZ8+eaV+/WrVqceaZZ8asWbNi0KBBSV93ySWXlKtv9erVE9Yk8xcX0ymZfmUNlVWV+wQAqIqqyl6vqtxnRNW6VwCAXPHkk0/GU089Fccdd1zk5+enff3u3bvH5MmTY9SoUUnvm8aMGRPTpk0rV9+qtLesSvcKAFRNK1asiMGDB8fHH3+csHb33XePBx54IPLy8tI6Q1XZc1WV+4yoWvcKAABA2XndCAAAFSsXPiMEqOxq164dDRs23OE/9erVK3eP+++/P3r27Bmvv/56GibOHQ7MBQAAACBn1KhRIyOHBPy3du3axYsvvhjf/e53k6qfMWNGPPfccyn3q1mzZsKabP918WT6JTN3Wesrw30CAFRFVWWvV1XuM9lrKsu9AgDkitq1a2e8R40aNeKaa66JRx55JKm9U0lJSdx0003l6lmV9pZV6V4BgKrnyy+/jMGDB8e8efMS1rZu3Tqee+65aNiwYdrnqCp7rqpyn8leU1nuFQAAgLLzuhEAACpOrnxGCFDZ/fznP48vv/xyh/8UFhZGUVFRrFy5MhYuXBjjxo2LG264IY477rho0KBB0n0++uijGDx4cLz66qsZvJvscmAuAAAAAFVSjRo1YsyYMXHYYYclVf+73/0u5V7JHAJcVFSU8vqpSKZfWQNcVeU+AQCqoqqy16sq9xlRte4VAKAqOvbYY+Ouu+5KqvbRRx+N5cuXp9yrKu0tq9K9AgBVy5o1a2LIkCExffr0hLUtWrSIl156KTp27JiRWarKnquq3GdE1bpXAAAAys7rRgAAqBi59BkhABHVq1ePxo0bR4cOHeKII46Iq666Kh577LH45JNPYsyYMXHAAQcktc66deti6NChMXny5AxPnB0OzAUAAACgyqpRo0Y89NBD0axZs4S1L774YqxatSqlPgUFBQlrNmzYkNLaqVq/fn3Cmlq1apVpzapynwAAVVFV2etVlfuMqFr3CgBQVZ1xxhlx5plnJqzbunVrPProoyn3qUp7y6p0rwBA1bF27do48sgj46233kpY26xZsxg/fnzsscceGZunquy5qsp9RlStewUAAKDsvG4EAIDsy7XPCAHYsTp16sRpp50Wr732Wvztb3+LFi1aJLxm/fr1cfLJJ8fq1auzMGFmOTAXAAAAgCqtWbNm8atf/Sph3ZYtW+LZZ59NqUfDhg0T1qxZsyaltVOVTL9GjRqVac1k7nP9+vWxdevWMq1bHpm4TwCAqsie9huVZU9bVX6mAABV3W9+85to0KBBwrqxY8em3MM+uuy908k+GgAoj/Xr18e3v/3teP311xPWNmnSJMaPHx89evTI6ExVZc9lH1323ulkHw0AAJA7cu1145YtW2Ljxo2l1hQUFDgwFwCAnVYufkYIQHJOPvnkmDt3bhx88MEJa5csWRIXXHBBFqbKLAfmAgAAAFDlnXnmmdG2bduEdZMmTUpp/aZNmyasKSwsTGntVCXTL5m5U6nP5r1m4j4BAKoie9qy906XTO1pd9afaZMmTbIwCQBA5dGkSZP44Q9/mLDujTfeSPkgLvvosvdOJ/toACBVX38RduLEiQlrGzduHOPHj4+99tor43PtrHsu70eX75pc/JnaRwMAAGRHrr1u9B0EAAAqs1z9jBCA5DVu3DjGjRsXhxxySMLav/3tbzFt2rQsTJU5DswFAAAAoMqrUaNGnHLKKQnr3njjjZTWT+YLNF988UVKa6dq5cqVCWvK+sWfmjVrRt26dRPWZfNeM3GfAABVkT3ttirDnjbXfqZbtmyJtWvXllpTv379yM/Pz9JEAACVx5lnnpmwZu3atTF79uyU1reP3pZ9NACwM9iwYUMMHz48JkyYkLC2UaNG8cILL0Tv3r0zP1jk3p4rwvvR/8k+GgAAgHTLtdeNvoMAAEBllcufEQJQNrVq1Yp//OMf0bp164S1v/nNb7IwUeY4MBcAAAAAImLo0KEJa/71r3+ltHbz5s0T1ixfvjyltVP1ySefJKzZZZddyrxurt1rpu4TAKCqybV9XoQ97X+qDPe5fPnyKCkpKbXG3h0AIDV77LFHdOrUKWFdqu+BR+Te/tI++hv20QDAf/v6i7Avv/xywtqGDRvG888/H/369cvCZF/JtT1XRNXZX1aV+7SPBgAAyC259rrRdxAAAKiMcv0zQgDKrkmTJnHLLbckrHvkkUdixYoVWZgoMxyYCwAAAAARMWDAgKhWrfS3y9atW5fSm4Ht27dPWJNMqCqdEgXG8vPzY9dddy3zurl2r8kE4zp06JD5QQAAdnK5ts+LsKf9T6nsaavKfQIA8JX99tsvYc3ChQtTXr+q7C+ryn0CAJXX11+EfemllxLWNmjQIJ5//vkYMGBAFib7Rq7tuSK8H/2f7KMBAABIN68bAQAgs3aGzwgBSM1JJ50UnTp1KrVmy5Yt8eqrr2ZpovRzYC4AAAAAREStWrWibdu2CetSOTC3TZs2kZ+fX2rNunXr4osvvijz2qlYsWJFrF+/vtSadu3aRfXq1cu8dseOHRPWLF68uMzrpmrRokUJa5KZGQCgqrOn3VZl2NNWlfsEAOArXbp0SViTyvvfX6sq+8uqcp8AQOWUyhdh99lnnyxMti3vR2+rMuwvq8p9AgAAkBqvGwEAIHN2ls8IAUhNXl5enHTSSQnrJk6cmIVpMsOBuQAAAADw/zVr1ixhTaIvQW1P9erVo127dgnrPvzwwzKvnYpk+iT6S2LluS5b95lsr1TvFQCgKrGnLXv/dMnUvSZzzfz586OkpKTMa6fC3h0AILMy9f731+yjv2EfDQDkovXr18ewYcOS+iJs/fr147nnnot99903C5P9L+9Hl71/uthHf8M+GgAAIHuqyutjAADItp3pM0IAUjdo0KCENVOnTs38IBniwFwAAAAA+P9q166dsGbDhg0prd2tW7eENR988EFKa5dVMn2SmTfV67J1n0VFRbFw4cJSaxo2bBitWrXKyjwAADu7XNrr2dN+I9U9bevWraNBgwal1mzYsCGWLl1a5rVTkcmfKQAAmX3/O8I++j/ZRwMAuebrL8K+/PLLCWvr168fzz//fOy3335ZmGzHcml/6f3ob9hHAwAAkAnJvAZbsGBBFBUVZWEarxsBAKgcdsbPCAFITdeuXRPWrFixIguTZIYDcwEAAADg/0vmMICCgoKU1u7du3fCmunTp6e0dllNmzYtYU0y86Z6Xbbuc/bs2bFly5ZSa/bee+/Iy8vLyjwAADu7XNrr2dN+I9U9bV5eXuy1114J6yrDzxQAgMy+/x1hH/3f7KMBgFzx9RdhX3nllYS19evXj+eeey4nvgibS/tL70d/wz4aAACATGjatGm0bdu21JrNmzfH3LlzszLPjBkzSn28Zs2aseeee2ZlFgAASMXO+hkhAKlp1qxZwpovvvgiC5NkhgNzAQAAAOD/+/zzzxPW1K1bN6W1+/Tpk7DmnXfeSWntskqmTzLzbk+nTp2icePGpdasXLkyFi1alNL6ZZHJ+wQAqIrsab9RWfa0ufIz3bhxY7z33nul1jRu3Dg6dOiQ8VkAACqrTL7/HWEfncoM5WUfDQAksn79+vj2t79dpi/CDhw4MAuTJZYre65k+3g/OrFc+ZnaRwMAAOSmXHndOH/+/Fi1alWpNXvuuWfUrFkz47MAAEAqdubPCAFITX5+fsKaNWvWZGGSzHBgLgAAAABExKZNm+Kjjz5KWJfMX9jann322Sdhzdtvvx2bN29Oaf1kbdq0KWFQrG7dutG9e/eUe/Tv3z9hzeTJk1NeP1nJ9BgwYEDG5wAAqCzsabdVGfa0yfxMs3Gfb731VhQVFZVaY+8OAFA+H374YcKaVN///pp9dNlmKC/7aACgNOvWrYuhQ4fGhAkTEtbm4hdhvR+9Lfvo9LGPBgAAyE258rrRdxAAANiZ7eyfEQKQmrVr1yasqVOnThYmyQwH5gIAAABAfPWFmOLi4lJr6tSpE82bN09p/VatWiX8gtT69etjypQpKa2frEmTJsWGDRtKrRk0aFDUqFEj5R6HHXZYwprx48envH6yEvXIy8uLQw45JONzAABUFva026oMe9pDDz008vLySq2ZNGlSbNy4MeUeyXjxxRcT1iTzMwEAYMeS2ad37NixXD3so79hHw0AVKR169bFt7/97Xj11VcT1tavXz+ef/75nPsirPejt2UfnT720QAAALkpV14fe90IAMDOqjJ8RghAahYtWpSwpl69epkfJEMcmAsAAAAAETFu3LiENZ07dy5Xj8MPPzwtc5RHMusPHjy4XD2Suc/nn38+4QHF5TF79uz46KOPSq3Ze++9Uz4AGQCgqrKn/UZl2NM2b948evXqVWrNxo0bk/oL8+WRjZ8pAEBV9sEHH8SCBQsS1mXjPXD76PSxjwYAtieVL8Lut99+WZis7Lwf/Q376PSxjwYAAMhNffv2jSZNmpRas2jRopg3b17GZti6dWvCA3OrVasWhx56aMZmAACAVFSmzwgBKLvp06cnrGnbtm3mB8kQB+YCAAAAUOUVFRXF3/72t4R1++yzT7n6HHnkkQlrHn744XL1KE1JSUk88sgjCeuOOOKIcvXZa6+9olWrVqXWfPLJJzFx4sRy9SnNQw89lLAmmZ8HAADbsqf9RmXZ0yazxt///vdy99mRf/3rX/HOO++UWtO6devo2bNnxmYAAKjs7r333oQ1tWvXLveeyz56W/bRAEC2leWLsA0aNMj5L8J6P/ob9tHpYR8NAACQu6pVq5bUHzDJ5OvGl19+OT799NNSa/bdd99o3LhxxmYAAICyqmyfEQJQds8//3zCmi5dumRhksxwYC4AAAAAVd6YMWNiyZIlCev233//cvU59NBDo3nz5qXWLFq0KKkPplLx8ssvx0cffVRqTd++fWP33XcvV5+8vLw46aSTEtbdd9995eqzI1u3bk3qAOTvfve7GekPAFCZ2dNuqzLsaU8++eSENY8//ngUFhaWu9f2jBkzJmHNSSedFHl5eRnpDwBQ2a1atSr+8Ic/JKwbMGBA5Ofnl6uXffS27KMBgGyqjF+E9X70tuyjy88+GgAAILcl89rzgQceiOLi4oz0T+Z1o+8gAACQSyrjZ4QAlM3KlSvjiSeeSFjXp0+fzA+TIQ7MBQAAAKBK++KLL+LKK69MWFe9evU48sgjy9WrRo0a8Z3vfCdh3W9/+9ty9dmRW2+9NWHNqaeempZeyazz0EMPJfwL7Kl4/PHHY/HixaXW7LXXXtGjR4+09wYAqOzsabdVGfa0PXv2jJ49e5ZaU1hYGHfffXe5e/23DRs2xJ/+9KeEden6mQIAVEWXX355rF69OmHd8OHD09LPPvob9tEAQLasX78+6S/CNmzYMF544YXYd999szBZ+Xg/elv20eVjHw0AAJD7jjzyyGjatGmpNfPnz48nn3wy7b2XLVsWDz/8cKk1yb5XAQAA2VBZPyMEoGxuuOGG2LhxY8K6gw8+OP9WpaAAAQAASURBVAvTZIYDcwEAAACosrZu3RqnnHJKfPb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      "text/plain": [
       "<Figure size 6400x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(\"logs-blog/fig2-log.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "38f5c105-b896-4e52-955e-9c65cce51e98",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average batch duration 0.4484109716212496\n",
      "average wait time 0.007295566026856299\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 6400x2400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot(\"logs-blog/fig3-log.txt\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "397e50b6-b5bf-46df-b8f1-cdbdb3ee5056",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "demo",
   "language": "python",
   "name": "demo"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
